numinal 0.1.0

Governance-as-code data cards for multi-party AI dataset sharing

numinal

Governance-as-code data cards for multi-party AI dataset sharing.

numinal generates structured, machine-readable metadata for AI datasets — covering provenance, bias analysis, access policies, and EU AI Act Article 10 compliance. Built on Croissant (MLCommons) with a governance extension layer.

Why

If you're publishing a shared AI dataset — especially under a UK Sovereign AI grant, an UKRI programme, or any cross-organisational data sharing initiative — you need to document:

• What the dataset contains (structure, provenance, collection methodology)
• What biases exist and how they've been addressed
• Who can access it, for what purposes, under what constraints
• How it maps to EU AI Act Article 10 requirements

No existing tool produces this documentation in a machine-readable format. numinal does.

Install

pip install numinal

Quick start

# Generate a data card from your dataset directory
numinal init ./my-dataset/ --tier 2

# Validate against compliance tiers
numinal validate ./my-dataset/numinal.yaml

# Check EU AI Act Article 10 compliance
numinal compliance ./my-dataset/numinal.yaml --regulation eu-ai-act-art-10

# Render as markdown for documentation
numinal render ./my-dataset/numinal.yaml -o datacard.md

What it looks like

Validation

$ numinal validate ./numinal.yaml

Tier 1 (discovery):           ✓ PASS — 8/8 required fields
Tier 2 (regulatory):          ✗ FAIL — 9/14 required fields
  Missing:
  - rai:measurementAssumptions (Art. 10(2)(d))
  - rai:suitabilityAssessment (Art. 10(2)(e))
  - rai:complianceGaps (Art. 10(2)(h))
  - rai:dataQualityAssessment (Art. 10(3))
  - rai:geographicContext (Art. 10(4))
Tier 3 (governed sharing):    ✗ FAIL — 14/23 required fields

Completeness: 67% (35/52 total fields)

Article 10 compliance

$ numinal compliance ./numinal.yaml --regulation eu-ai-act-art-10

EU AI Act Article 10 — 13 sub-requirements checked:

  ✓ 10(2)(a) Design choices
  ✓ 10(2)(b) Collection processes and origin
  ✓ 10(2)(b)+ Original collection purpose disclosure
  ✓ 10(2)(c) Data preparation operations
  ✗ 10(2)(d) Measurement assumptions — rai.measurementAssumptions missing
  ✓ 10(2)(e) Suitability assessment
  ✓ 10(2)(f) Bias examination
  ✓ 10(2)(g) Bias mitigation measures
  ✗ 10(2)(h) Compliance gaps identified — rai.complianceGaps missing
  ✓ 10(3) Data quality criteria
  ✓ 10(4) Geographic and contextual characteristics
  — 10(5) Special category data safeguards (skipped: not applicable)
  ✓ 10(6) Dataset role distinction

Score: 10/11 requirements met

Compliance tiers

Tier	Name	Purpose	Who needs it
T1	Discovery	Dataset is findable, understandable, usable	Any dataset publisher
T2	Regulatory	Supports EU AI Act Article 10 compliance	Publishers whose data may be used in high-risk AI
T3	Governed sharing	Full multi-party access control with audit trail	Cross-organisational dataset sharing

T3 ⊇ T2 ⊇ T1. Start at T1, add fields as you need them.

How it works

numinal init scans your dataset directory and auto-detects:

• File types, sizes, SHA-256 checksums
• Column names, data types, null rates, cardinality (CSV/TSV)
• Existing README, LICENSE, Croissant metadata

It generates a numinal.yaml — the human-authored source of truth — with TODO markers for fields you need to fill in manually. Run numinal validate to see what's missing at each tier.

Schema

The numinal data card extends Croissant with two additional layers:

Layer	Standard	What it covers
Dataset structure	Croissant 1.0 (MLCommons)	Files, schemas, splits, ML semantics
Responsible AI	Croissant-RAI 1.0 (MLCommons)	Bias, fairness, collection methodology
Governance	Croissant-GOV 0.1 (numinal)	Access policies, DUAs, compliance, metering

Every numinal data card is simultaneously valid Croissant metadata. The governance fields live in their own namespace — tools that don't understand gov: fields simply ignore them.

Controlled vocabularies are sourced from:

• Organisation types: UK Cabinet Office Public Bodies Handbook, Companies House
• Data use purposes: GA4GH Data Use Ontology (DUO), extended with AI-specific terms
• High-risk domains: EU AI Act Annex III
• Security classifications: UK Government Security Classifications Policy (GSCP)
• Policy expression: W3C ODRL 2.2 via DPV-ODRL profile

See the specification for full details.

Example

See examples/uk-health-ai-corpus.yaml for a complete T3 data card demonstrating all three schema layers.

Commands

Command	Status	Purpose
numinal init	✓	Generate a data card from a dataset directory
numinal validate	✓	Validate against compliance tiers
numinal compliance	✓	Check against EU AI Act Article 10
numinal render	✓	Render as markdown
numinal diff	planned	Compare two data card versions

License

Apache-2.0